Mapping webpages to page groups

ABSTRACT

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for mapping webpages to page groups. The program and method provide for receiving plural Uniform Resource Locators (URLs), each URL of the plural URLs corresponding to a respective webpage of a website; generating a distance matrix with pairwise distances between the plural URLs; performing hierarchical clustering based on the distance matrix, to generate a dendrogram in which the plural URLs are arranged in hierarchical clusters; and determining, based on the dendrogram, a predicted page group for each of the plural URLs.

CLAIM OF PRIORITY

This Application claims the benefit of priority of U.S. ProvisionalApplication No. 63/336,780, filed Apr. 29, 2022, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to web session analysis,including mapping webpages to page groups.

BACKGROUND

Web analysis solutions provide for the collection and analysis ofwebsite data. Such solutions may provide for capturing user interactionwith respect to webpage visits.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of an experience analyticssystem, in accordance with some examples, that has both client-side andserver-side functionality.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 illustrates an architecture configured to map webpages to pagegroups, in accordance with some examples.

FIGS. 5A and 5B illustrate example dendrograms in which URLs are groupedinto clusters, in accordance with some examples.

FIG. 6 illustrates an architecture configured to determine conditionsfor a set of webpages, in accordance with some examples.

FIG. 7 is a flowchart illustrating a process for mapping webpages topage groups, in accordance with some examples.

FIG. 8 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 9 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

Web analysis solutions provide for the collection and analysis ofwebsite data. Example web analysis tools include the tracking andrecording of session events corresponding to user interactions,automated website zone identification, session replay, statisticalanalysis of collected data, and the like. Such tools may require that awebsite be configured, for example, with respect to properly mapping thepages of the website to page groups.

The disclosed embodiments provide for automatically creating mappings ofwebpages to page groups, with qualitative conditions. The experienceanalytics system as described herein provides for creating page groups(e.g., meaningful page groups), and estimating conditions that buildeach page group.

In one or more embodiments, the disclosed embodiments provide forreceiving plural Uniform Resource Locators (URLs), each URL of theplural URLs corresponding to a respective webpage of a website, andgenerating a distance matrix with pairwise distances between the pluralURLs. Hierarchical clustering is performed based on the distance matrix,to generate a dendrogram in which the plural URLs are arranged inhierarchical clusters. Based on the dendrogram, a predicted page groupis determined for each of the plural URLs.

Networked Computing Environment

FIG. 1 is a block diagram showing an example experience analytics system100 that analyzes and quantifies the user experience of users navigatinga client's website, mobile websites, and applications. The experienceanalytics system 100 can include multiple instances of a member clientdevice 102, multiple instances of a customer client device 104, andmultiple instances of a third-party server 108.

The member client device 102 is associated with a client of theexperience analytics system 100, where the client that has a websitehosted on the client's third-party server 108. An agent of the client(e.g., a web administrator, an employee, an operator, etc.) can be theuser of the member client device 102.

Each of the member client devices 102 hosts a number of applications,including an experience analytics client 112. Each experience analyticsclient 112 is communicatively coupled with an experience analyticsserver system 106 and third-party servers 108 via a network 110 (e.g.,the Internet). An experience analytics client 112 can also communicatewith locally-hosted applications using Applications Program Interfaces(APIs).

The member client devices 102 and the customer client devices 104 canalso host a number of applications including Internet browsingapplications (e.g., Chrome, Safari, etc.). The experience analyticsclient 112 can also be implemented as a platform that is accessed by themember client device 102 via an Internet browsing application orimplemented as an extension on the Internet browsing application.

Users of the customer client device 104 can access client's websitesthat are hosted on the third-party servers 108 via the network 110 usingthe Internet browsing applications. For example, the users of thecustomer client device 104 can navigate to a client's online retailwebsite to purchase goods or services from the website.

The third-party server 108 may include data relating to websites, datarelating to webpages, other, like, data, and any combination thereof.The third-party server 108 may be a local web source(s), remote websource(s), or any combination thereof, including a cloud-basednetwork(s), distributed network(s), and the like. Examples of thethird-party server 108 include, but are not limited to, repositories ofwebpage information, repositories of webpage element or zoneinformation, servers configured to provide “live” webpages, other, like,sources, and any combination thereof.

While a user of the customer client device 104 is navigating a client'swebsite on an Internet browsing application, the Internet browsingapplication on the customer client device 104 can also execute aclient-side script (e.g., JavaScript (.*js)) such as an experienceanalytics script 114. In one example, the experience analytics script114 is hosted on the third-party server 108 with the client's websiteand processed by the Internet browsing application on the customerclient device 104. The experience analytics script 114 can incorporate ascripting language (e.g., a .*js file or a .json file).

In certain examples, a client's native application (e.g., ANDROID™ orIOS™ Application) is downloaded on the customer client device 104. Inthis example, the client's native application including the experienceanalytics script 114 is programmed in JavaScript leveraging a SoftwareDevelopment Kit (SDK) provided by the experience analytics server system106. The SDK includes Application Programming Interfaces (APIs) withfunctions that can be called or invoked by the client's nativeapplication.

In one or more embodiments, the experience analytics script 114 isconfigured to collect activity relating to a client's interaction withthe third-party server 108 content through a webpage displayed on thecustomer client device 104. In one example, the experience analyticsscript 114 records data including the changes in the interface of thewebpage being displayed on the customer client device 104, the elementson the webpage being displayed or visible on the interface of thecustomer client device 104, the text inputs by the user into thewebpage, a movement of a mouse (or touchpad or touch screen) cursor,user scrolls, and mouse (or touchpad or touch screen) clicks on theinterface of the webpage. In addition, and with proper user permissions,the experience analytics script 114 may be configured to collectactivity data features including, customer client device 104 type,website/application type, customer client device 104 geolocation,customer client device 104 internet protocol (IP) address, uniformresource locators (URLs) accessed by the customer client device 104,customer client device 104 screen resolution, and/or referrer URLs.

The experience analytics script 114 transmits the data to the experienceanalytics server system 106 via the network 110. In another example, theexperience analytics script 114 transmits the data to the third-partyserver 108 and the data can be transmitted from the third-party server108 to the experience analytics server system 106 via the network 110.As such, the experience analytics script 114 is configured to collectactivity relating to a client's interaction with web server content(e.g., content from the third-party server 108) through a webpagedisplayed on the customer client device 104.

In one or more embodiments, the experience analytics script 114 may beincluded within the source code of a webpage, such as the hypertextmarkup language (HTML) code underlying such a webpage, where such sourcecode is hosted by the third-party server 108 (e.g., web server). Where auser of the customer client device 104 connects to the third-partyserver 108 and requests to visit a given webpage, the underlying codefor the webpage is downloaded to the customer client device 104 andrendered thereupon, including the experience analytics script 114,providing for user interaction with the webpage, as well as for datacollection by the experience analytics script 114.

In one or more embodiments, the member client device 102 includes anexperience analytics client 112. The experience analytics client 112 isa platform, program, service, or the like, configured to provide helpagents, and the like, with the ability to view details of a livesession. For example, the experience analytics client 112 is configuredto provide user interfaces to display one or more features of a livesession, including, without limitation, live session events, historicalreplay data, and the like, as well as any combination thereof. Theexperience analytics client 112 may be configured to provide a helpagent with a unique per-session view, the unique per-session viewcorresponding to a single user's current session. The experienceanalytics client 112 may be configured to provide the unique view uponthe help agent's activation of a unique link (e.g., a live sessionlink), where such a unique link may be sent to the member client device102 upon a user's interaction with a “live support” or similar button orfeature, as may be included in a webpage which a user is visiting on thecustomer client device 104.

The experience analytics client 112 may be further configured toidentify, based on the contents of the unique link, one or more relevantlive replay data features including, without limitation, live sessionevents, historical recorded events, and the like, and to collect,receive, or otherwise access such data features. Specifically, theexperience analytics client 112 may be configured to access live sessionevents by opening a connection to a short-latency queue (SLQ) 124.

In addition, the experience analytics client 112 may be configured tocollect or receive data relevant to one or more previous sessionsincluding, as examples and without limitation, session replays, sessionreplay analytics, and the like. The experience analytics client 112 maybe configured to provide for collection, receipt, or the like, of suchdata, as may be relevant to such previous sessions, from one or moresources including, without limitation, the database 300, and the like,as well as any combination thereof.

Following collection, receipt, or the like, of live and historicalsession data, the experience analytics client 112 provides fordisplaying user interface(s) with one or more of such data features to ahelp agent, providing for agent review of current and historical sessiondata. Such presentation, through the member client device 102, providesfor short-term view of session data combined with long-term persistentview of session data. In this regard, data exchanged between theexperience analytics client 112 and the experience analytics serversystem 106 may include functions (e.g., commands to invoke functions) aswell as payload data (e.g., website data, texts reporting errors,insights, merchandising information, adaptability information, images,graphs providing visualizations of experience analytics, session replayvideos, zoning and overlays to be applied on the website, etc.).

The experience analytics server system 106 supports various services andoperations that are provided to the experience analytics client 112.Such operations include transmitting data to and receiving data from theexperience analytics client 112. Data exchanges to and from theexperience analytics server system 106 are invoked and controlledthrough functions available via user interfaces (UIs) of the experienceanalytics client 112.

The experience analytics server system 106 provides server-sidefunctionality via the network 110 to a particular experience analyticsclient 112. While certain functions of the experience analytics system100 are described herein as being performed by either an experienceanalytics client 112 or by the experience analytics server system 106,the location of certain functionality either within the experienceanalytics client 112 or the experience analytics server system 106 maybe a design choice. For example, it may be technically preferable toinitially deploy certain technology and functionality within theexperience analytics server system 106 but to later migrate thistechnology and functionality to the experience analytics client 112where a member client device 102 has sufficient processing capacity.

Turning now specifically to the experience analytics server system 106,an Application Program Interface (API) server 116 is coupled to, andprovides a programmatic interface to, application servers 120. Theapplication servers 120 are communicatively coupled to a database server126, which facilitates access to a database 300 that stores dataassociated with experience analytics processed by the applicationservers 120. Similarly, a web server 118 is coupled to the applicationservers 120, and provides web-based interfaces to the applicationservers 120. To this end, the web server 118 processes incoming networkrequests over the Hypertext Transfer Protocol (HTTP) and several otherrelated protocols.

The Application Program Interface (API) server 116 receives andtransmits message data (e.g., commands and message payloads) between themember client device 102 and the application servers 120. Specifically,the Application Program Interface (API) server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the experience analytics client 112 or the experience analyticsscript 114 in order to invoke functionality of the application servers120. The Application Program Interface (API) server 116 exposes to theexperience analytics client 112 various functions supported by theapplication servers 120, including generating information on errors,insights, merchandising information, adaptability information, images,graphs providing visualizations of experience analytics, session replayvideos, zoning and overlays to be applied on the website, etc.

The application servers 120 host a number of server applications andsubsystems, including for example an experience analytics server 122.The experience analytics server 122 implements a number of dataprocessing technologies and functions, particularly related to theaggregation and other processing of data including the changes in theinterface of the website being displayed on the customer client device104, the elements on the website being displayed or visible on theinterface of the customer client device 104, the text inputs by the userinto the website, a movement of a mouse (or touchpad) cursor and mouse(or touchpad) clicks on the interface of the website, etc. received frommultiple instances of the experience analytics script 114 on customerclient devices 104. The experience analytics server 122 implementsprocessing technologies and functions, related to generating userinterfaces including information on errors, insights, merchandisinginformation, adaptability information, images, graphs providingvisualizations of experience analytics, session replay videos, zoningand overlays to be applied on the website, etc. Other processor andmemory intensive processing of data may also be performed server-side bythe experience analytics server 122, in view of the hardwarerequirements for such processing.

In one or more embodiments, the experience analytics server 122 isconfigured to execute instructions for streaming live sessions (e.g.,live browsing sessions). As is relevant to the execution of instructionsfor streaming live sessions, live sessions are real-time ornear-real-time representations of user journeys through a webpage or setof webpages, including the users' interactions therewith.

The experience analytics server 122 may be configured to activate a“live mode” or other, similar, program, routine, or the like, inresponse to the receipt, collection, or the like, of one or more “livemode” trigger commands, instructions, or the like, as may be sent by theexperience analytics script 114, as described above. Such “live mode”routines may include, without limitation, increasing session eventprocessing frequency, initiating one or more post-to-SLQ processes, suchas may be applicable to the population of the short-latency queue (SLQ)118 with live replay events and data, and the like.

The SLQ 124 may provide for collection, receipt, or the like, of sessionevents, including session events in the order of collection or receipt.The SLQ 124 is a memory, storage, or other, like, component, configuredto provide real-time or near-real-time storage of session events, suchas clicks, scrolls, text entries, and the like, in the order in whichsuch session events are generated during a user's session, as well assubsequent retrieval or transmission of such stored events, including inorder, in real-time or near-real-time, as described hereinbelow. The SLQ124 may be configured as a virtual component, as a physical component,or in a hybrid physical-virtual configuration.

In one or more embodiments, the database 300 is configured to archivedata permanently or semi-permanently. The database 300 may be configuredto store information received from one or more web third-party servers108 (e.g., based on a request from the experience analytics server 122to the third-party servers 108 for information, such as webpagecontent), customer client devices 104, and other, like, components, aswell as to store data relevant to the operation of the experienceanalytics server 122 and any outputs therefrom. The database 300 may bea local system, a remote system, or a hybrid remote-local system.Further, the database 300 may be configured as a fully-physical system,including exclusively physical components, as a virtualized system,including virtualized components, or as a hybrid physical-virtualsystem. Examples of devices which may be configured as a database 300 inthe experience analytics system 100 include, without limitation, localdatabase hardware, cloud storage systems, remote storage servers, other,like, devices, and any combination thereof. Further, the database 300may be directly connected to the experience analytics server 122, suchas without an intermediate connection to the network 110, including viaconnections similar or identical to those described with respect to thenetwork 110.

In one or more embodiments, the database 300 may be configured to storeor otherwise archive data relating to one or more sessions, including,without limitation, user interactions, user sessions, other, like, data,and any combination thereof. Further, the database 300 may be configuredto transfer, to and from the experience analytics server 122, datanecessary for the execution of the methods described herein, and maystore or otherwise archive experience analytics server 122 inputs,experience analytics server 122 outputs, or both.

As an example of a potential use-case involving the experience analyticssystem 100, as may be relevant to the descriptions provided herein, auser may attempt to access a website to purchase a product. The usermay, through the customer client device 104, and a browser app includedtherein, generate a request to access the website. The request, whenreceived by the third-party server 108, may configure the third-partyserver 108 to send a copy of webpage(s) of the website to the customerclient device 104, including the experience analytics script 114. Thedatabase 300 may store a copy of the webpage(s) from the third-partyservers 108 (e.g., based on a request from the experience analyticsserver 122 to the third-party servers 108). The experience analyticsserver 122 may provide such copy to the customer client device 104.During the course of the customer client's session, the experienceanalytics script 114 may collect session data and transmit such data tothe experience analytics server 122 for storage in the database 300.

In addition, where the user at the customer client device 104 encountersan issue (e.g., an error such a defective checkout button, userconfusion, and/or another type of issue), the user may engage a livehelp support feature (e.g., implemented by the experience analyticsserver 122), for example, by selecting a chat button. In this regard,the help support feature includes a chat component, which allows asupport agent at the member client device 102 to chat with the user atthe customer client device 104. Moreover, the help support featureallows the user to connect with the help agent, causing the experienceanalytics script 114 to employ a script interface (e.g., a JavascriptAPI) to make data available for the member client device 102 (e.g., suchthat when the live session link/button is pressed, this data is visibleto the agent), and to send a live mode trigger to the experienceanalytics server system 106. Following receipt of the live mode triggerby the experience analytics server system 106, the user's session datamay be pushed to the SLQ 124 of the experience analytics server 122, inreal-time or near-real-time. The experience analytics server 122 sendsthe live session link to the member client device 102, where the livesession link is selectable by the help agent.

Following a help agent's activation of the live session link, theexperience analytics server 122 may be configured to provide livesession replay to the member client device 102. For example, theexperience analytics server 122 generates a combined SLQ 124 anddatabase 300 data feed, and provides the combined data feed to the helpagent at the member client device 102, in real-time or near-real-time,permitting the help agent to view the user's live session, and providesuggestions regarding how the user can better engage with the website.The merging allows the help agent to seek back (e.g., rewind) to viewwhat happened, even before the website visitor at the customer clientdevice 104 pressed the chat button.

System Architecture

FIG. 2 is a block diagram illustrating further details regarding theexperience analytics system 100 according to some examples.Specifically, the experience analytics system 100 is shown to comprisethe experience analytics client 112 and the experience analytics server122. The experience analytics system 100 embodies a number ofsubsystems, which are supported on the client-side by the experienceanalytics client 112 and on the server-side by the experience analyticsserver 122. These subsystems include, for example, a data managementsystem 202, a data analysis system 204, a zoning system 206, a sessionreplay system 208, a journey system 210, a merchandising system 212, anadaptability system 214, an insights system 216, an errors system 218,and an application conversion system 220.

The data management system 202 is responsible for receiving functions ordata from the processors 804, the experience analytics script 114executed by each of the customer client devices 104, and the third-partyservers 108. The data management system 202 is also responsible forexporting data to the processors 804 or the third-party servers 108 orbetween the systems in the experience analytics system 100. The datamanagement system 202 is also configured to manage the third-partyintegration of the functionalities of experience analytics system 100.

The data analysis system 204 is responsible for analyzing the datareceived by the data management system 202, generating data tags,performing data science and data engineering processes on the data.

The zoning system 206 is responsible for generating a zoning interfaceto be displayed by the processors 804 via the experience analyticsclient 112. The zoning interface provides a visualization of how theusers via the customer client devices 104 interact with each element onthe client's website. The zoning interface can also provide anaggregated view of in-page behaviors by the users via the customerclient device 104 (e.g., clicks, scrolls, navigation). The zoninginterface can also provide a side-by-side view of different versions ofthe client's website for the client's analysis. For example, the zoningsystem 206 can identify the zones in a client's website that areassociated with a particular element in displayed on the website (e.g.,an icon, a text link, etc.). Each zone can be a portion of the websitebeing displayed. The zoning interface can include a view of the client'swebsite. The zoning system 206 can generate an overlay including datapertaining to each of the zones to be overlaid on the view of theclient's website. The data in the overlay can include, for example, thenumber of views or clicks associated with each zone of the client'swebsite within a period of time, which can be established by the user ofthe processors 804. In one example, the data can be generated usinginformation from the data analysis system 204.

The session replay system 208 is responsible for generating the sessionreplay interface to be displayed by the processors 804 via theexperience analytics client 112. The session replay interface includes asession replay that is a video reconstructing an individual user'ssession (e.g., visitor session) on the client's website. The user'ssession starts when the user arrives into the client's website and endsupon the user's exit from the client's website. A user's session whenvisiting the client's website on a customer client device 104 can bereconstructed from the data received from the user's experienceanalytics script 114 on customer client devices 104. The session replayinterface can also include the session replays of a number of differentvisitor sessions to the client's website within a period of time (e.g.,a week, a month, a quarter, etc.). The session replay interface allowsthe client via the processors 804 to select and view each of the sessionreplays. In one example, the session replay interface can also includean identification of events (e.g., failed conversions, angry customers,errors in the website, recommendations or insights) that are displayedand allow the user to navigate to the part in the session replaycorresponding to the events such that the client can view and analyzethe event.

The journey system 210 is responsible for generating the journeyinterface to be displayed by the processors 804 via the experienceanalytics client 112. The journey interface includes a visualization ofhow the visitors progress through the client's website, page-by-page,from entry onto the website to the exit (e.g., in a session). Thejourney interface can include a visualization that provides a customerjourney mapping (e.g., sunburst visualization). This visualizationaggregates the data from all of the visitors (e.g., users on differentcustomer client devices 104) to the website, and illustrates the visitedpages and in order in which the pages were visited. The client viewingthe journey interface on the processors 804 can identify anomalies suchas looping behaviors and unexpected drop-offs. The client viewing thejourney interface can also assess the reverse journeys (e.g., pagesvisitors viewed before arriving at a particular page). The journeyinterface also allows the client to select a specific segment of thevisitors to be displayed in the visualization of the customer journey.

The merchandising system 212 is responsible for generating themerchandising interface to be displayed by the processors 804 via theexperience analytics client 112. The merchandising interface includesmerchandising analysis that provides the client with analytics on: themerchandise to be promoted on the website, optimization of salesperformance, the items in the client's product catalog on a granularlevel, competitor pricing, etc. The merchandising interface can, forexample, comprise graphical data visualization pertaining to productopportunities, category, brand performance, etc. For instance, themerchandising interface can include the analytics on conversions (e.g.,sales, revenue) associated with a placement or zone in the clientwebsite.

The adaptability system 214 is responsible for creating accessibledigital experiences for the client's website to be displayed by thecustomer client devices 104 for users that would benefit from anaccessibility-enhanced version of the client's website. For instance,the adaptability system 214 can improve the digital experience for userswith disabilities, such as visual impairments, cognitive disorders,dyslexia, and age-related needs. The adaptability system 214 can, withproper user permissions, analyze the data from the experience analyticsscript 114 to determine whether an accessibility-enhanced version of theclient's website is needed, and can generate the accessibility-enhancedversion of the client's website to be displayed by the customer clientdevice 104.

The insights system 216 is responsible for analyzing the data from thedata management system 202 and the data analysis system 204 surfaceinsights that include opportunities as well as issues that are relatedto the client's website. The insights can also include alerts thatnotify the client of deviations from a client's normal business metrics.The insights can be displayed by the processors 804 via the experienceanalytics client 112 on a dashboard of a user interface, as a pop-upelement, as a separate panel, etc. In this example, the insights system216 is responsible for generating an insights interface to be displayedby the processors 804 via the experience analytics client 112. Inanother example, the insights can be incorporated in another interfacesuch as the zoning interface, the session replay, the journey interface,or the merchandising interface to be displayed by the processors 804.

The errors system 218 is responsible for analyzing the data from thedata management system 202 and the data analysis system 204 to identifyerrors that are affecting the visitors to the client's website and theimpact of the errors on the client's business (e.g., revenue loss). Theerrors can include the location within the user journey in the websiteand the page that adversely affects (e.g., causes frustration for) theusers (e.g., users on customer client devices 104 visiting the client'swebsite). The errors can also include causes of looping behaviors by theusers, in-page issues such as unresponsive calls to action and slowloading pages, etc. The errors can be displayed by the processors 804via the experience analytics client 112 on a dashboard of a userinterface, as a pop-up element, as a separate panel, etc. In thisexample, the errors system 218 is responsible for generating an errorsinterface to be displayed by the processors 804 via the experienceanalytics client 112. In another example, the insights can beincorporated in another interface such as the zoning interface, thesession replay, the journey interface, or the merchandising interface tobe displayed by the processors 804.

The application conversion system 220 is responsible for the conversionof the functionalities of the experience analytics server 122 asprovided to a client's website to a client's native mobile applications.For instance, the application conversion system 220 generates the mobileapplication version of the zoning interface, the session replay, thejourney interface, the merchandising interface, the insights interface,and the errors interface to be displayed by the processors 804 via theexperience analytics client 112. The application conversion system 220generates an accessibility-enhanced version of the client's mobileapplication to be displayed by the customer client devices 104.

Data Architecture

FIG. 3 is a schematic diagram illustrating database 300, which may bestored in the database 300 of the experience analytics server 122,according to certain examples. While the content of the database 300 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 300 includes a data table 302, a session table 304, azoning table 306, an error table 310, an insights table 312, amerchandising table 314, and a journeys table 308.

The data table 302 stores data regarding the websites and nativeapplications associated with the clients of the experience analyticssystem 100. The data table 302 can store information on the contents ofthe website or the native application, the changes in the interface ofthe website being displayed on the customer client device 104, theelements on the website being displayed or visible on the interface ofthe customer client device 104, the text inputs by the user into thewebsite, a movement of a mouse (or touchpad or touch screen) cursor andmouse (or touchpad or touch screen) clicks on the interface of thewebsite, etc. The data table 302 can also store data tags and results ofdata science and data engineering processes on the data. The data table302 can also store information such as the font, the images, the videos,the native scripts in the website or applications, etc.

The session table 304 stores session replays for each of the client'swebsites and native applications. Session replays may include sessionevents associated with browsing sessions. In one or more embodiments,session events correspond to user interactions with one or moreelements, sections, zones (e.g., stored in association with the zoningtable 306 discussed below), or the like, of a webpage. Examples ofsession events include, but are not limited to, user input of enteringtext in a text box, clicking a button with a mouse, tapping a buttonwith a touchscreen, navigating to a webpage, navigating away from awebpage, scrolling up or down on the webpage, hovering over a webpageelement, and the like, as well as any combination thereof. Sessionreplay and recording may be executed by generating one or more logs,lists, and the like, of such events (e.g., as detected by an experienceanalytics script 114) included in a webpage accessed by a user of thecustomer client device 104. Such logs, lists, and the like may be storedin the session table 304, and may include one or more event descriptorsincluding the event type, the event target, such as a specific button ortext box, the event time, and the like, as well as combinations thereof.

The zoning table 306 stores data related to the zoning for each of theclient's websites and native applications including the zones to becreated and the zoning overlay associated with the websites and nativeapplications. The journeys table 308 stores data related to the journeyof each visitor to the client's website or through the nativeapplication. The error table 310 stores data related to the errorsgenerated by the errors system 218 and the insights table 312 storesdata related to the insights generated by the insights table 312.

The merchandising table 314 stores data associated with themerchandising system 212. For example, the data in the merchandisingtable 314 can include the product catalog for each of the clients,information on the competitors of each of the clients, the dataassociated with the products on the websites and applications, theanalytics on the product opportunities and the performance of theproducts based on the zones in the website or application, etc.

FIG. 4 illustrates an architecture 400 configured to map webpages topage groups, in accordance with some examples. For explanatory purposes,the architecture 400 is primarily described herein with reference to themember client device 102 and the experience analytics server 122 of FIG.1 . However, the architecture 400 may correspond to one or more othercomponents and/or other suitable devices.

In one or more embodiments, the architecture 400 is implemented by thedata analysis system 204. For example, the data analysis system 204 mayimplement the architecture 400 with respect to configuring a website fordata analysis. As noted above, the experience analytics system 100provides web analysis tools, including the tracking and recording ofsession events corresponding to user interactions with a website,automated website zone identification, session replay, statisticalanalysis of collected data, and the like. Such tools may require that awebsite be configured, for example, with respect to properly mapping thepages of the website to page groups. Thus, the architecture 400 isconfigured to automatically create mappings with qualitative conditionsand page categories. Further, the architecture 400 is configured tocreate page groups (e.g., meaningful page groups), estimate conditionsthat build each page group, and estimate the page category of each pagegroup.

As shown in FIG. 4 , the architecture 400 provides for an automaticmapping module 404 configured to determine page groups from URLs. Theautomatic mapping module 404 is configured to receive sampled URLs 402as input, and to determine page groups 406 that correspond to thewebpage.

At a high level, the architecture 400 provides for sampling a predefinednumber of URLs (e.g., N URLs) randomly from one website. For example, Nmay initially be set to 1000 URLs, corresponding to the number ofsampled URLs 402, the sampled URLs 402 being representative of thewebsite. The automatic mapping module 404 includes components 408-414which are configured to perform the clustering and page grouppredictions with respect to the sampled URLs 402. In one or moreembodiments, the architecture 400 is configured to: compute a pairwiseURL distance matrix with the sampled URLs 402 (e.g., via the pairwiseURL distance matrix computation module 408); cluster the URLs byapplying agglomerative hierarchical clustering on the distance matrix(e.g., via the URL clustering module 410); estimate conditions thatcharacterize each cluster in the dendrogram, where each cluster in thedendrogram is a potential page group candidate (e.g., via the URLpattern and conditions computation module 412, discussed further belowwith respect to FIG. 6 ); and estimate the clusters corresponding to thepage groups 406 (e.g., via the cluster prediction module 414).

Regarding the sampled URLs 402, the data analysis system 204 isconfigured to use a set of URLs from a single website. In one or moreembodiments, a user (e.g., administrator) may manually build page groupsinto a mapping using conditions on URLs. Further, created page groupsmay have a standardized meaning. In one or more embodiments, in a retailwebsite, a home page group, a product, cart, checkout, and the like arecreated. For example, the URLs satisfying conditions of page groups mayrepresent at least 90% of all URLs traffic with respect to the website,and URLs with high traffic may be deemed “prioritized.”

In one example, the data analysis system 204 is configured to sample NURLs for each website by randomly sampling N/2 URLs weighted withtraffic (e.g., based on user views). Randomly sampling URLs withoutweighting on traffic would result in this URL not being picked. As such,the data analysis system 204 may randomly sample N/2 URLs from theremaining samples.

As noted above, a user (e.g., administrator) may manually build pagegroups using conditions on URLs. Since a human user used them to labelthe page groups in a specific category, the corresponding terms may bedeemed as “prioritized.” Such conditions may be used to discern theprioritized terms in URLs and to leverage such terms. For example, theset of terms corresponds to a prioritized terms library (e.g., stored inthe database 300).

In one or more embodiments, the pairwise URL distance matrix computationmodule 408 is configured to assign weights to terms, so as to prioritizethe prioritized terms relative to other (e.g., regular) terms. In one ormore embodiments, a URL=host+path+query+cvars. It is noted thatconventionally, URL=host (or host)+path+query. A path is a sequence oflevels, and a query is a set of key values attributes. In addition,cvars are custom variables (key values attributes) sent from the website(e.g., hosted by the third-party server 108) to the experience analyticsserver system 106 for each visited URL by a user of the website. Termsare path levels, query keys and cvars keys. In one or more embodiments,notations for terms as described herein are defined as follows:

URL: Host\path?query∥cvars

path: level 1\level 2\ . . . Level i\ . . . \Level N=[l₁, l₂, . . . ,l_(i), . . . l_(N)]

query: k1=v2 & k2=v2={k1, v1, k2=v2}, keys=[k1, k2]; values=[v1, v2]

cvars: k3<v3˜k4<v4={k3: v3, k4: v3}; keys=[k3, k4]; values [v3, v4]

terms: path levels & query keys & cvars key={l₁, l₂, . . . , l₄, k1, k2,k3, k4}

In one or more embodiments, the automatic mapping module 404 isconfigured to estimate website prioritized terms. For a set of URLs fora website, the automatic mapping module 404 extracts website distinctterms (path levels, query keys, cvars keys) for all the URLs. Theautomatic mapping module 404 then computes the similarity of those termswith the prioritized terms library. For example, terms are consideredsimilar if their levenshtein ratio >80%, where the levenshtein ratio isthe levenshetein edit distance normalized over the sum of the length ofthe strings.

In one or more embodiments, each of path_prioritized_terms,query_prioritized_terms, and cvars_prioritized_terms are automaticallyfound using the prioritized terms library. Distinct terms can beextracted from each URL part (e.g., path levels, query keys, cvarskeys). In general, terms include path levels+query keys+cvars keys. Assuch, a term is considered prioritized if the conditions are met withrespect to path levels, query keys and cvar keys as described below.

For path levels, the conditions are met if: (1) a path level is similarto at least one path term in the prioritized terms library (e.g.,levenshtein ratio >80%); and (2) the path level is not present in morethan 95% of URLs. In this regard, prioritized terms present in a highamount of URLs, or high traffic, may be considered an edge case and arenot considered prioritized.

For query keys, the conditions are met if: (1) the query key is similarto at least one query key in the prioritized terms library (e.g.,levenshtein ratio >80%); (2) the key is not present in more than 95% ofURLs (e.g., URL count wise or traffic count wise); and (3) the key'svalues maximum does not also exceed 95% of URLs (e.g., URL count wise ortraffic count wise).

For cvars keys, the conditions are met if: (1) the cvars key is similarto at least one cvars key in the prioritized terms library (e.g.,levenshtein ratio >80%); (2) the key is not present in more than 95% ofURLs (e.g., URL count wise or traffic count wise); and (3) the key'svalues maximum does not also exceed 95% of URLs (e.g., URL count wise ortraffic count wise); (4) the key takes less than 30 distinct values; (5)the key takes more than 5 distinct values; and (6) the key takesmeaningful values. In other words, for each value of the key, itssimilarity (e.g., levenshtein ratio) is computed with all the terms inthe path prioritized terms library, and the maximum value is kept. Ifthe median of those maximum values is >50%, it is considered that thekey takes meaningful values.

Thus, if the above conditions are met for the path levels, query keysand cvar keys, the term is considered prioritized and weights areassigned accordingly. In one or more embodiments, weights are set foreach term as follows:

path_weights={term: 0.9 for term in path_prioritized_terms else 0.1}

query_weights={term: 0.9 for term in query_prioritized_terms else 0.1}

cvars_weights={term: 0.9 for term in cvars_prioritized_terms else 0.1}

In addition, weights are set for each URL part as follows:

path_weight=sum(term traffic for each term in path_prioritized_terms)

query_weight=max(term traffic for each term in query_prioritized_terms)

cvars_weight=max(term traffic for each term in cvars_prioritized_terms)

host_weight=0.5*min(path_weight, query_weight, cvars_weight)−host isalways less prioritized than other variables.

As noted above, the pairwise URL distance matrix computation module 408of the automatic mapping module 404 is configured to compute a pairwiseURL distance matrix with the sampled URLs 402. In one or moreembodiments, the pairwise URL distance matrix computation module 408implements various formulas in order to generate the pairwise URLdistance matrix, based on the above-described notations for terms.

In one or more embodiments, the pairwise URL distance matrix computationmodule 408 defines functions needed for the distance formula. Alldefined functions are bounded between [0, 1]. For example, suchdefinitions include:

F: Distance (URL1, URL2): distance between URL1 and URL2.

G: Similarity (URL1, URL2): Similarity between URL1 and URL2.

H: List_Similarity (list1, list2): Similarity between 2 lists

I: Key_values_similarity (dict1, dict2): Similarity between 2 dicts

J: Keys_similarity (dict1, dict2): Similarity between the keys of 2dicts

K: values_similarity (dict1, dict2): Similarity between the values of 2dicts

L: string_similarity (str1, str2): Similarity between 2 string values

Furthermore, the pairwise URL distance matrix computation module 408defines all weights used in the distance. For a givenpath_prioritized_terms list, query_prioritized_terms list,cvars_prioritized_terms list, terms may be defined as follows:

path_terms_weights: ptw={term: 0.9 if term in path_prioritized_termsElse 0.1; for term in website distinct path terms}

query_terms_weights: qtw={term: 0.9 if term in query_prioritized_termsElse 0.1; for term in website distinct query terms}

cvars_terms_weights: ctw={term: 0.9 if term in cvars_prioritized_termsElse 0.1; for term in website distinct cvars terms}

path_weight: pw=Sum (traffic_percentage (term); for term inpath_prioritized_terms) (=1% if path prioritized terms Ø)

query_weight: gw=Max (traffic_percentage (term); for term inquery_prioritized_terms) (=1% if query prioritized terms Ø

cvars_weight: cw=Max (traffic_percentage (term); for term incvars_prioritized_terms) (=1% if cvars prioritized terms Ø)

host_weight: hw=0.5×Min (pw, qw, cw)

${{where}{traffic\_ percentage}({term})} = \frac{{\sum{{traffic}({URL})}};{{for}{}{URL}{in}{URLs}{where}{term}{occur}}}{{\sum{{traffic}({URL})}};{{URL}{in}{ALL}{URLs}}}$

In one or more embodiments, it is noted that prioritized path levelsrarely co-occur in a single URL contrary to prioritized query/cvars keywhere one prioritized key can possibly occur in all URLs. This relatesto the sum operator for path vs. max for query and cvars.

Moreover, the pairwise URL distance matrix computation module 408 maydefine formulas as follows:F(URL1,URL2)=1−G(URL1,URL2)G(URL1,URL2)=[hw×L(host1,host2)+pw×H(Path1,Path2)+qw×I(query1,query2)+cw×I(cvars1,cvars2)]/[hw+pw+qw+cw]L (x, y); Given 2 values x and y, is x equal to y?H (x, y); Given 2 lists x and y of arbitrary length, define thesimilarity as the average of similarities of lists of same length:1. Transform pair (x, y) to 2 pairs of same length: (Longer_list,shorter_list_padded) and (shorter_list, longer_list_cropped) where

-   -   (longer_list, shorter_list)=(x, y) if length (x)>, length (y)        Else (y, x)    -   shorter_list_padded: pad shorter_list with empty values to match        longer_list length    -   longer_list_cropped: crop longer_list values (from the end) to        match shorter_list length        2. H(x, y)=[M(longer_list,        shorter_list_padded)+M(longer_list_cropped, shorter_list)]/2        where    -   M(a, b)=Sum (weighting DOT term_equality)/Sum (weighting)    -   term_equality=[L(a_(i), b_(i)); for i in [1, length (a)]

${{weighting} = \left\lbrack \frac{{Pt{w\left( a_{i} \right)}} + {Pt{w\left( b_{i} \right)}}}{2} \right\rbrack};$

-   -    for i in [1, length (a)]        I(x, y); Given 2 dictionaries x and y, I(x, y)=[J(x, y)+K(x,        y)]/2, Let c_keys be the common keys between keys(x) & keys(y)        J(x, y) is the weighted average ratio of common keys

${{J\left( {x,y} \right)} = {\left\lbrack {\frac{{Sum}{}\left( {{{qtw}(k)};{{for}k{in}{c\_ keys}}} \right)}{{Sum}\left( {{{qtw}(k)};{{for}k{in}x}} \right)} + \frac{{Sum}\left( {{{qtw}(k)};{{for}k{in}{c\_ keys}}} \right)}{{Sum}{}\left( {{{qtw}(k)};{{for}k{in}y}} \right)}} \right\rbrack/2}};$

-   -   qtw used for query, ctw for cvars        K(x, y) is the weighted average of values equality of common        keys    -   k(x, y)=sum(weighting DOT values_equality)/sum (weighting)    -   where values_equality=[L(x[k], y[k]); for k in c_keys]    -   weighting=[qtw [k], for k in c_keys]; qtw used for query, ctw        for cvars

As noted above, the automatic mapping module 404 includes the URLclustering module 410. The URL clustering module 410 is configured toperform an agglomerative hierarchical clustering on the sampled URLs402, to generate a dendrogram.

In one or more embodiments, the agglomerative hierarchical clustering asperformed by the URL clustering module 410 corresponds to a bottom-upclustering approach. For example, the URL clustering module 410considers all data points (e.g., all of the sampled URLs 402) asclusters (e.g., group of URLs). Clusters are merged up the hierarchyuntil one large cluster is formed. The agglomerative hierarchicalclustering results in a dendrogram, which is a diagram representing allthe progressive merging steps from N data points to the large onecluster as depicted in FIGS. 5A and 5B below. As discussed below, finalclusters/page groups may be estimated by detecting prioritized nodes.

In one or more embodiments, the clusters are determined by gridsearching the number of clusters between a predefined range of clusters(e.g., between 10 and 40 clusters, an arbitrary range). For example, theURL clustering module 410 is configured to compute the silhouette scorefor each n_clusters (e.g., chosen as the decision metric as it does notnecessarily require ground truth clustering knowledge). As describedherein, the silhouette score is a metric used to calculate the“goodness” of a clustering technique. The silhouette score has a valuethat ranges from −1 to 1, where 1 indicates clusters are well apart fromeach other and clearly distinguished, and 0 indicates clusters areindifferent (e.g., the distance between clusters is not significant).

The URL clustering module 410 then picks N (e.g., the chosen number ofclusters) such that N is in the set of n_clusters satisfying silhouettescore >a preset percentile (e.g., 96% percentile) of all silhouettescores, and N is as small as possible. In this manner, it is possible toachieve a high score without having many clusters.

As noted above, the URL pattern and conditions computation module 412 isconfigured to automatically characterize each page group, as opposed tosimply listing out the URLs for each cluster without characterization.For example, in one or more embodiments, the URL pattern and conditionscomputation module 412 is configured to detect patterns, by identifyrepeating terms that constitute a pattern. When a pattern is detected,the URL pattern and conditions computation module 412 generatesconditions that would characterize the set of URLs. The URL pattern andconditions computation module 412 is discussed further below withrespect to FIG. 6 .

Thus, with respect to estimating clusters, the URL clustering module 410is configured to determine an initial N number of clusters (e.g., withthe silhouette score grid searching method), and the URL pattern andconditions computation module 412 is configured to compute conditionsfor each cluster. As noted above, the automatic mapping module 404further includes the cluster prediction module 414, which is configuredto estimate the clusters corresponding to the page groups 406.

In this regard, the automatic mapping module 404 is configured toestimate clusters using two approaches. In the first estimationapproach, the URL clustering module 410 determines an initial number ofclusters as discussed above. In particular, the number of clusters N isselected such N is in the set of n_clusters satisfying silhouettescore >a preset percentile (e.g., 96% percentile) of all silhouettescores, and N is as small as possible.

In the second estimation approach, the URL pattern and conditionscomputation module 412 computes conditions for each cluster. Theautomatic mapping module 404 further includes the cluster predictionmodule 414. In one or more embodiments, the cluster prediction module414 is configured to detect prioritized nodes. For example, a node(cluster) is deemed prioritized if (1) its set of conditions contains aprioritized term, (2) the node has at least 2 URLs or at least 0.5%traffic and (3) the node has less than 80% of global number of URLs orless than 80% of traffic.

In addition, for the second estimation approach, the cluster predictionmodule 414 estimates (guesses) new clusters. For example, the clusterprediction module 414 is configured to (1) start from the dendrogramroot node, (2) develop nodes recursively in a top down approach, (3)stop developing when a prioritized node is found, and (4) for all treebranches without a prioritized node, collapse the branches to maximumwithout collapsing a prioritized node (e.g., referred to as pseudoprioritized nodes).

Moreover, the automatic mapping module 404 is configured such that, ifthe number of new clusters found is greater than a preset number(e.g., >50), then the initial clustering per the above first estimationapproach is not overwritten (e.g., to prevent the tree from developingtoo far to result in burst clustering).

Thus, the automatic mapping module 404 provides for sampling N URLsrandomly from one website (e.g., N=1000 by default) as described abovewith respect to the sampled URLs 402. Next, the pairwise URL distancematrix computation module 408 computes the pairwise URL distance matrix.The URL clustering module 410 then provides for clustering URLs byapplying agglomerative hierarchical clustering on the distance matrix.Next, the URL pattern and conditions computation module 412 computesURLs patterns and conditions for each cluster of the dendrogram. Fromthe resulted dendrogram, the cluster prediction module 414 provides fordetermining (e.g., guessing) the clusters as page groups.

Moreover, the automatic mapping module 404 is configured toautomatically characterize each page group of the page groups 406, basedon the output provided by URL pattern and conditions computation module412 (e.g., as discussed further with respect to FIG. 6 below). Asdescribed herein, the URL pattern and conditions computation module 412is configured to detect patterns, by identifying repeating terms thatconstitute a pattern. When a pattern is detected, the URL pattern andconditions computation module 412 generates conditions that wouldcharacterize the set of URLs.

FIGS. 5A and 5B illustrate example dendrograms 502-504 in which URLs aregrouped into clusters, in accordance with some examples. As noted above,a dendrogram is a diagram representing all the progressive merging stepsfrom N data points (e.g., the sampled URLs 402).

In the example of FIG. 5A, the dendrogram 502 depicts circlesrepresenting different clusters for nodes A-K. In the example of FIG.5B, the dendrogram 504 depicts a tree structure at which edges terminateat the nodes A-K.

In both of the examples of FIGS. 5A-5B, all data points (e.g., nodes A-Krepresenting the sampled URLs 402) are grouped into clusters. Theclusters are merged up the hierarchy until one large cluster is formed.

FIG. 6 illustrates an architecture 600 configured to determineconditions for a set of webpages, in accordance with some examples. Forexplanatory purposes, the architecture 600 is primarily described hereinwith reference to the experience analytics system 100 of FIG. 1 .However, the architecture 600 may correspond to one or more othercomponents and/or other suitable devices.

In one or more embodiments, the architecture 600 may be implemented bythe data analysis system 204. In addition, the architecture 600corresponds to the URL pattern and conditions computation module 412 ofFIG. 4 .

For a set of URLs coming from a single website, the cardinality ofvalues is typically finite and low with respect to the levels in paths,key presence/value in query or cvars that make up the URL. Thearchitecture 600 is configured to identify repeating terms in a set ofURLs that would constitute a pattern. From the identified pattern(s),the architecture 600 generates conditions that characterize the set ofURLs.

As shown in the example of FIG. 6 , the architecture 600 includes anautomatic conditions module 602 configured to determine conditions fromURLs. The automatic conditions module 602 is configured to receive asubset of URLs 604 and all URLs 606 as input. The automatic conditionsmodule 602 includes a pattern detector 608 and pattern to conditionstranslator 610 configured to analyze the subset of URLs 604, and togenerate the positive conditions 620. The automatic conditions module602 further includes a complementary URLs detector 612 configured toreceive the subset of URLs 604 and all URLs 606 as input. The automaticconditions module 602 further includes a pattern detector 614, a patternto conditions translator 616, and a conditions subtractor 618, whichcombined are configured to generate the negative conditions 622 asoutput.

In one or more embodiments, the subset of URLs 604 of FIG. 6 correspondswith the nodes for each cluster determined by the URL clustering module410 of FIG. 4 . Moreover, all URLs 606 of FIG. 6 corresponds with thesampled URLs 402, which are representative of the website as discussedabove. Alternatively, all URLs 606 of FIG. 6 may correspond to all URLsthat comprise the website.

As noted above, URL=host (or host)+path+query+cvars. Regarding thepattern detector 608, the pattern for the subset of URLs 604 may beconsidered as follows: pattern(subset of URLs)=pattern(subset of host)and pattern(subset of paths) and pattern(subset of queries) andpattern(subset set of cvars). In general, the pattern detector 608 isconfigured to identify statistically repeating patterns in fields.

For example, for a given field (e.g., host or a path level or aquery/cvars key or a query/cvars key's value): (1) if the possiblevalues of the field are N values (e.g., where N is a fixed value whichis not relatively high), then those possible values constitute a patternfor that field (e.g., field=value 1 or value 2 or . . . value N); (2) ifcondition (1) is not met, and if the possible values of the field are Mvalues (e.g., where M is relatively high and each value occurs roughlyonce or twice), then the field corresponds to a pseudo-pattern (e.g.,associated with pseudo-ids values, where the pattern is represented by aspecial character such as “*”); (3) if conditions (1) and (2) are notmet, the field's pattern is set to the values that occur more than X %(e.g., where X is fixed and predefined), so as to only keep values thatare statistically high. It is noted that the pattern in condition (3)can be empty (e.g., where all values are present <X %). Based onconditions (1)-(3), it is possible that a pattern can partially describea field.

With respect to a host for the subset of URLs 604, the pattern detector608 may implement a host pattern detector. In one or more embodiments,for a set of hosts, the pattern detector 608 is configured to detectpatterns as follows: (1) if the distinct values <N, then the host'spattern is set to all of those values; (2) if there are too many (e.g.,M) unique values appearing rarely (e.g., once or twice), the host'spattern is set to pseudo-ids and flagged as (3) otherwise, the host'spattern is set to values where the ratio presence >X %. In one or moreembodiments, X is denoted host_ratio_thr and N is denotedhost_cardinality_thr as discussed further below.

By way of non-limiting example, patterns for hosts are generated asfollows based on the following: 9 hosts, N=6, X=20%.

host values host1 www.mywebsite.com host2 www.order.mywebsite.com host3www.order.mywebsite.com host4 www.pro.mywebsite.com host5www.prod.mywebsite.com host6 www.mywebsite.com host7 www.mywebsite.comhost8 www.staging.mywebsite.com host9 www.pre-prod.mywebsite.com

The corresponding values distribution is as follows:

value % occurrence www.mywebsite.com 33.33% 3 www.order.mywebsite.com 22.2% 2 www.pro.mywebsite.com  12.5% 1 www.prod.mywebsite.com  12.5% 1www.staging.mywebsite.com  12.5% 1 www.pre-  11.1% 1 prod.mywebsite.com

Based on the above, the pattern detector 608 is configured to applyconditions as follows: (1) too many distinct values; (2) not all valuesalways appear once or twice; and (3) keep values with rationpresence >20%. Thus, the pattern detector 608 may set patterns asfollows:

host pattern={“www.mywebsite.com”: 0.33, “www.order.mywebsite.com”:0.22}

With respect to paths for the subset of URLs 604, the pattern detector608 may implement a path pattern detector. In one or more embodiments,the pattern detector 608 is configured to: (1) pad paths to the longestpath length with an special empty value; (2) for each path level,compute the level's values distributions (a) if its distinct values <Nthen the level's pattern=all of those values, (b) if too many uniquevalues appearing rarely (e.g., once or twice), the level's pattern ispseudo-ids and flagged as “*” and (c) otherwise, the level'spattern=values with ratio presence >X %; (3) any level after theshortest path length is considered optional. In one or more embodiments,X is denoted path_ratio_thr, and N is denoted path_cardinality_thr asdiscussed further below.

By way of non-limiting example, patterns for paths are generated asfollows based on the following: 9 paths, N=6, X=30%.

level1 level2 level3 level4 path 1 p 65678 cart payment path2 p 165353checkout delivery path3 p 1677777 checkout delivery path4 p 6541 cartdelivery path5 p 7766 cart ‘ ’ path6 p 98 shoes ‘ ’ path7 p 097 bring‘ ’ path8 p 556 hello ‘ ’ path9 p 15 hi ‘ ’

Level 1 value % occurrence p 100% 8

Level 1 indicates one unique value. As such, the pattern detector 608may set the pattern as follows: pattern={“p”: 1.0}.

Level 2 value % occurrence 65678 11.1% 1 165353 11.1% 1 1677777 11.1% 16541 11.1% 1 7766 11.1% 1 98 11.1% 1 097 11.1% 1 556 11.1% 1 15 11.1% 1

Level 2 indicates too many distinct values, and all values always appearonce or twice. As such, the pattern detector 608 may set the pattern asfollows: pattern={“*”: 0}.

Level 3 value % occurrence cart 33.33% 3 checkout  22.2% 2 shoes  12.5%1 bring  12.5% 1 hello  12.5% 1 hi  11.1% 1

Level 3 indicates too many distinct values, not all values always appearonce or twice, and to keep values with ratio presence >30%: cart with33.33% ratio. As such, the pattern detector 608 may set the pattern asfollows: pattern={“cart”: 0.33}.

Level 4 value % occurrence ‘ ’ 55.55% 5 delivery 33.33% 3 payment  11.1%1

Level 4 indicates only 3 unique values. As such, the pattern detector608 is configured to keep all of them and set the pattern as follows:pattern={“Ø”: 0.555, “delivery”: 0.333, “payment”: 0.111}.

Moreover, based on the above, the pattern detector 608 is configured toset the path pattern as follows:

path pattern={“required”: 3, “levels”: [{“p”: 1.0}, {“*”: 0}, {“Ø”:0.555}, {“delivery”: 0.333}, {“payment”: 0.111}]}.

With respect to queries (or cvars) for the subset of URLs 604, thepattern detector 608 may implement a query/cvars pattern detector. For aset of queries (or cvars), the pattern detector 608 is configured to:(1) compute distinct keys: union of the keys appearing in all queries(or cvars); (2) determine that a key is considered frequent if its ratiopresence >X1%, where key patterns considers only frequent keys; (3) foreach frequent key, (a) determine its values in each query (or cvars). Ifthe value not present, use an empty special value, (b) compute the key'svalue values distributions, such that (i) if distinct values <N then thekey's value's pattern=all of those values, and (ii) if too many uniquevalues appearing rarely (e.g., once or twice), the key's value's patternis pseudo-ids and flagged as “*”, (iii) else the key's value'spattern=values with ratio presence >X2%.

By way of non-limiting example, patterns for queries are generated asfollows based on the following: 9 queries, X1=30%, N=6, X2=20%. In oneor more embodiments, X1 is denoted query_k_ratio_thr (orcvars_k_ratio_thr), X2 is denoted query_v_ratio_thr (orcvars_k_ratio_thr), and N is denoted query_v_cardinality_thr (orcvars_v_cardinality_thr) as discussed further below.

k1 k2 k3 k4 query1 v1 v2 v3 ‘ ’ query2 v1 v4 v6 v7 query3 v8 ‘ ’ v9 ‘ ’query4 v1 ‘ ’ ‘ ’ ‘ ’ query5 ‘ ’ ‘ ’ v10 ‘ ’ query6 ‘ ’ ‘ ’ v11 ‘ ’query7 ‘ ’ ‘ ’ v3 ‘ ’ query8 v1 ‘ ’ v3 ‘ ’ query9 v1 ‘ ’ v12 ‘ ’

The corresponding keys' values distribution are as follows:

key % occurrence k1 66.66% 6 k2 22.22% 2 k3 88.88% 8 k4 11.11% 1

As shown above, k1 and k3 are the only keys with ratio presence >30%.Thus, the pattern detector 608 may only consider the values of thosekeys. In this regard, k1's value values distributions are as follows:

value % occurrence v1 55.5% 5 ‘ ’ 33.33%  3 v8 11.1% 1

The above indicates 3 unique values only. As such, the pattern detector608 is configured to set the pattern as follows: pattern={“v1”: 0.555,“Ø”: 0.333, “v8”: 0.111}.

Moreover, k3's value values distribution is as follows:

value % occurrence v3 33.33%  3 v6 11.1% 1 v9 11.1% 1 ‘ ’ 11.1% 1 v1011.1% 1 v11 11.1% 1 v12 11.1% 1

The above indicates too many distinct values, not all values alwaysappear once or twice, and to keep values with a ratio presence >20%. Assuch, the pattern detector 608 is configured to set the pattern asfollows: pattern={“v3”: 0.33}.

Moreover, the pattern detector 608 is configured to set the querypattern as follows:

query pattern={“k1”: {“ratio”: 0.666, “values”: {“v1”: 0.555, “Ø”:0.333, “v8”: 0.111}}, “k3”: {“ratio”: 0.888, “values”: {“v3”: 0.33}},}.

In one or more embodiments, the pattern detector 608 is configured toimplement a pattern format as follows:

{

“host”: {“host_1”: 0.5, “host_2”: 0.3},

“path”: {“required”: 2, “levels”: [{“11_value1”: 1.0}, {“*”: 0},{“l3_value1”: 0.5, “l3_value2”: 0.3}]},

“query”: {

-   -   “q_key_1”: {“ratio”: 0.4, “values”: {“*”: 0}},    -   “q_key_2”: {“ratio”: 1.0, “values”: {“key_2_v1”: 0.9,        “key_2_v2”: 0.1}}        },

“cvars”: {

-   -   “c_key_1”: {“ratio”: 1.0, “values”: {“key_1_v1”: 1.0}},    -   “c_key_2”: {“ratio”: 1.0, “values”: { }},

},

}

Based on the above, and with respect to hosts, the pattern detector 608is configured such that the host takes at least 2 possible values:host_1 present 50% of the time, host_2 present 30% of the time. The pathhas 2 required levels (level 1 and 2), where level 1 always takes thesame value: 11_value, level 2 takes pseudo-ids values (values appearingonce or twice), level 3 takes at least 2 possible values: 13_value1present 50% of the time, 13_value2 present 30% of the time (e.g., level3 values ratio cannot sum to 1 as it is not a required level).

Regarding queries, the pattern detector 608 is configured such that: keyq_key_1 is present 40% of the time and takes pseudo_ids values; keyq_key_2 is always present, and takes only 2 possible values: key_2_v1present 90% of the time and key_2_v2 present 10% of the time.

Regarding cvars, the pattern detector 608 is configured such that: keyc_key_1 is always present, and always takes one value key_1_v1; and keyc_key_2 is always present, and takes some values without a clearpattern.

Thus, the pattern detector 608 is configured to identify patterns forthe host, path, query and cvars fields of the subset of URLs 604. Theidentified patterns are provided as input to the pattern to conditionstranslator 610, which is configured to generate conditions from theidentified patterns. In one or more embodiments, the pattern toconditions translator 610 is configured to look for segments of URLswhose ratio sum=1, indicating always present.

By way of non-limiting example, the pattern to conditions translator 610generates conditions as follows. For the example {“v1”: 0.7, “v2”: 0.3},this field takes at least v1 and v2 as possible values, but becausetheir presence ratio sums to 1, this field takes only v1 and v2 asvalues. The pattern to conditions translator 610 generates a condition:field=v1 or field=V2.

For the example {“v1”: 0.5, “v2”: 0.3}, this field takes at least v1 andv2 as possible values. However, because v1 and V2 do not cover all thevalues cases (e.g., as summing to 80%), the pattern to conditionstranslator 610 does not generate a condition on this field.

For the example “key”: {“ratio”: 0.8, “values”: {“key_1_v1”: 0.8}}, thekey is present 80% of the time. As such, the pattern to conditionstranslator 610 does not generate any condition on this key.

Regarding hosts, the pattern to conditions translator 610 may implementa host to conditions translator configured as follows: if the sum ofvalues ratio=1, generate a condition else empty condition. For example,the {“host1”: 0.7, “host2”: 0.3}=>{“host”: {“host1”, “host2”}}. Inanother example, the {“host1”: 0.5, “host2”: 0.3}=>{“host”: { }}.

Regarding paths, the pattern to conditions translator 610 may implementa path to conditions translator as follows: conditions are generatedonly on required levels; for each level, the same logic of valuessumming to 1 is applied. For example, “path”: {“required”: 2, “levels”:[{“l1_value1”: 1.0}, {“*”: 0}, {“l3_value1”: 0.5, “l3_value2”:0.3}]}=>{“path”: {0: {“l1_value1”}}}. In another example, “path”:{“required”: 0, “levels”: [{“l1_value1”: 0.8}, {“*”: 0}]}=>{“path”: {}}.

Regarding queries and/or cvars, the pattern to conditions translator 610may implement a query/cvars to conditions translator as follows:conditions are only generated for keys whose ratio=1 (e.g., alwayspresent); for each key's values, the same logic of values summing to 1is applied; exclude from any key's values, special values: “*” (forpseudo-ids) and “0” (for empty value). For example, “k1”: {“ratio”: 1.0,“values”: {“v1”: 0.5, “v2”: 0.5}}=>=>{“query”: {“k1”: {“v1”, “v2”}}}. Inanother example, “k1”: {“ratio”: 0.8, “values”: {“v1”: 0.3, “v2”:0.5}}=>=>{“query”: { }}. In another example, “k1”: {“ratio”: 1.0,“values”: {“v1”: 0.5, “v2”: 0.3}}=>=>{“query”: {“k1”: { }}}. In anotherexample, “k1”: {“ratio”: 1.0, “values”: {“v1”: 0.5, “v2”: 0.3, “Ø”:0.2}}=>=>{“query”: {“k1”: { }}}.

Thus, with respect to the positive conditions 620, the pattern detector608 is configured to implement a conditions format as follows:

{

“host”: {“host_1”},

“path”: {0: {“l0_value1”}, 2: {“l2_value1”, “l2_value2”}},

“query”: {

-   -   “q_key_1”: { },    -   “q_key_2”: {“q_k2_v1”, “q_k2_v2”}        },

“cvars”: { },

}

Based on the above, the pattern to conditions translator 610 isconfigured such that the host=host_1. For the path, level 0=10_value1and level 2=(l2_value1 or l2_value2). For the query, q_key_1 is presentand q_key_2=(q_k2_v1 or q_k2_v2).

Thus, the pattern to conditions translator 610 is configured to outputpositive conditions 620 based on the subset of URLs 604, the patterndetector 608 and the pattern to conditions translator 610. In one ormore embodiments, the automatic conditions module 602 is configured tocompute the positive conditions 620 (e.g., conditions satisfying thesubset of URLs 604) but also to compute negative conditions 622 (e.g.,conditions that discriminate the subset of URLs 604 from the remainingURLs of all URLs 606). As such, the architecture 600 provides forreceiving both the subset of URLs 604 and all URLs 606.

At a high level, given a set of “URLs A” (e.g., the subset of URLs 604)belonging to all website “URLs B” (e.g., all URLs 606), the automaticconditions module 602 computes the positive conditions of URLs A (e.g.,the positive conditions 620, or “conditions A”). From URLs B, theautomatic conditions module 602 is configured to determine all URLs notbelonging to A and satisfying conditions A (e.g., as “URLs C”). Theautomatic conditions module 602 is configured to compute conditions forURLs C (e.g., as “conditions C”). As such, the negative conditions ofURLs A (e.g., “negative conditions A”) can be computed as: conditionsC−conditions A. In addition, the conditions of A can be computed as:conditions A+negative conditions A.

For example, with respect to FIG. 6 , given the subset of URLs 604belonging to all URLs 606 of a given website, the pattern detector 608and the pattern to conditions translator 610 are configured to computethe positive conditions 620 (e.g., conditions A) of the subset of URLs604. From all URLs 606, the complementary URLs detector 612 selects allremaining URLs not belonging to the subset of URLs 604 and satisfyingthe positive conditions 620. The pattern detector 614 and the pattern toconditions translator 616 are configured to compute conditions for theremaining URLs (e.g., conditions C). In this regard, the patterndetector 614 and the pattern to conditions translator 616 are configuredto perform similar functions as the pattern detector 608 and the patternto conditions translator 610, but with respect to the remaining URLs.The conditions subtractor 618 computes the negative conditions 622 forsubset of URLs 604 (e.g., as conditions C−conditions A). In addition,the automatic conditions module 602 computes the final (net) conditionsof A (e.g., as conditions A+negative conditions A).

Thus, with respect to the positive conditions 620 and the negativeconditions 622, the pattern detector 608 is configured to implement aconditions format as follows:

{

“positive”: {

-   -   “host”: [ ],    -   “path”: {0: [“legal-cookies” ]},    -   “query”: { },    -   “cvars”: {        -   “page type”: [“CGU” ],        -   “local path”: [ ],    -   },

},

“negative”: {

-   -   “host”: [“it.website.com”, “us.website.com” ],    -   “path”: {1: [“pbl-terms-conditions-outlet-stores” ]},    -   “query”: { },    -   “cvars”: {“redirection”: [“social” ]},

},

}

Based on the above, the automatic conditions module 602 is configuredsuch that host NOT=(it.website.com OR us.website.com). For the path,level 0=legal-cookies and level 1NOT=pbl-terms-conditions-outlet-stores. For the cvars, page type=CGU,local path is present, and redirection NOT=social.

In one or more embodiments, the automatic conditions module 602 isconfigured to detect alias similarity. For example, an alias correspondsto a set of conditions manually input by the user. These conditionstranslate into a set of URLs for a fixed time duration. Comparing twoalias corresponds with comparing their set of URLs. The automaticconditions module 602 is configured to determine that aliases areduplicates if they have the same set of URLs. For example, similarity isdetermined as follows:

Similarity(alias 1, alias 2)=IOU(URLs 1, URLs2)=length(Intersection(URLs1, URL2))/length(Union(URLs1, URLs2))

The automatic conditions module 602 is configured to determine thatalias 1 is a duplicate of alias 2 if Similarity(alias 1, alias 2)=1.

Alternatively or in addition, an alias may correspond to a set ofautomatically generated conditions. Two aliases are duplicates if theyhave the same conditions. For example, similarity is determined asfollows:

Similarity(alias 1, alias 2)=Similarity(conditions1, conditions2); whereconditions are generated from the automatic conditions module 602 (URLsof alias)

Similarity(conditions1, conditions2)=(Similarity(host conditions 1, hostconditions 2)+Similarity(path conditions 1, path conditions2)+Similarity(query conditions 1, query conditions 2)+Similarity(cvarsconditions 1, cvars conditions 2))/4

Similarity(host conditions 1, host conditions 2)=IOU(host values 1, hostvalues 2)

Similarity(path conditions 1, path conditions 2)=Mean over levels of(IOU(level values 1, level values 2)); if level not present inconditions, use an empty set of values

Similarity(query conditions 1, query conditions 2)=Mean over keys of(IOU(key values 1, key values 2)); if key not present in conditions, usean empty set of values.

In one or more embodiments, the automatic conditions module 602 isconfigured to apply the above logic of query to cvars.

By way of non-limiting example, the automatic conditions module 602 maybe configured to make the above-noted thresholds ofhost_cardinality_thr, path_cardinality_thr, query_v_cardinality_thr,cvars_v_cardinality_thr a dynamic function of URLs length as follows:for positive pattern detector: 1% of URLs, min to 10, max to 21; fornegative pattern detector: 1% of URLs, min to 70, max to 101. Inaddition, one or more of the pattern to conditions translator 610 or thepattern to conditions translator 616 may be configured to look forsegments of URLs whose ratio presence sum >99.9% (instead of =100%).

Thus, the automatic conditions module 602, corresponding to the URLpattern and conditions computation module 412, is configured toautomatically characterize each page group of the page groups 406, asopposed to simply listing out the URLs for each cluster withoutcharacterization. In doing so, the automatic conditions module 602detects patterns, by identify repeating terms that constitute a pattern.When a pattern is detected, the automatic conditions module 602generates conditions (e.g., conditions A+negative conditions A, as notedabove) that would characterize the set of URLs.

FIG. 7 is a flowchart illustrating a process 700 for mapping webpages topage groups, in accordance with some examples. For explanatory purposes,the process 700 is primarily described herein with reference to theexperience analytics server 122 of FIG. 1 . However, one or more blocks(or operations) of the process 700 may be performed by one or more othercomponents, and/or by other suitable devices. Further for explanatorypurposes, the blocks (or operations) of the process 700 are describedherein as occurring in serial, or linearly. However, multiple blocks (oroperations) of the process 700 may occur in parallel or concurrently. Inaddition, the blocks (or operations) of the process 700 need not beperformed in the order shown and/or one or more blocks (or operations)of the process 700 need not be performed and/or can be replaced by otheroperations. The process 700 may be terminated when its operations arecompleted. In addition, the process 700 may correspond to a method, aprocedure, an algorithm, etc.

The experience analytics server 122 receives plural Uniform ResourceLocators (URLs), each URL of the plural URLs corresponding to arespective webpage of a website (block 702). The experience analyticsserver 122 generates a distance matrix with pairwise distances betweenthe plural URLs (block 704).

The experience analytics server 122 may access, from a database, a setof terms, the set of terms having been predetermined as prioritized. Thepairwise distances may be based on weights applied to URLs, of theplural URLs, having at least one term appearing within the set of terms.The experience analytics server 122 may extract distinct terms for theplural URLs, and compute a similarity of the distinct terms with the setof terms, based on a levenshtein ratio for the similarity exceeding apredefined value.

The experience analytics server 122 performs hierarchical clusteringbased on the distance matrix, to generate a dendrogram in which theplural URLs are arranged in hierarchical clusters (block 706). Theexperience analytics server 122 may compute URL patterns and conditionsfor each cluster within the hierarchical clusters.

The hierarchical clustering may include agglomerative hierarchicalclustering. Determining the hierarchical clusters may include performinggrid searching with respect to a preset range of clusters, computing asilhouette score for each cluster within the preset range of clusters,and determining the hierarchical clusters based on the computedsilhouette scores. Determining the hierarchical clusters may be furtherbased on identifying nodes within the dendrogram having conditions thatinclude at least one term within the set of terms.

The experience analytics server 122 determines, based on the dendrogram,a predicted page group for each of the plural URLs (block 708).

Machine Architecture

FIG. 8 is a diagrammatic representation of the machine 800 within whichinstructions 810 (e.g., software, a program, an application, an applet,an application, or other executable code) for causing the machine 800 toperform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 810 may cause the machine 800 toexecute any one or more of the methods described herein. Theinstructions 810 transform the general, non-programmed machine 800 intoa particular machine 800 programmed to carry out the described andillustrated functions in the manner described. The machine 800 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 800 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 800 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 810, sequentially or otherwise,that specify actions to be taken by the machine 800. Further, while onlya single machine 800 is illustrated, the term “machine” shall also betaken to include a collection of machines that individually or jointlyexecute the instructions 810 to perform any one or more of themethodologies discussed herein. The machine 800, for example, maycomprise the processors 804 or any one of a number of server devicesforming part of the experience analytics server 122. In some examples,the machine 800 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 800 may include processors 804, memory 806, and input/outputI/O components 802, which may be configured to communicate with eachother via a bus 840. In an example, the processors 804 (e.g., a CentralProcessing Unit (CPU), a Reduced Instruction Set Computing (RISC)Processor, a Complex Instruction Set Computing (CISC) Processor, aGraphics Processing Unit (GPU), a Digital Signal Processor (DSP), anApplication Specific Integrated Circuit (ASIC), a Radio-FrequencyIntegrated Circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 808 and aprocessor 812 that execute the instructions 810. The term “processor” isintended to include multi-core processors that may comprise two or moreindependent processors (sometimes referred to as “cores”) that mayexecute instructions contemporaneously. Although FIG. 8 shows multipleprocessors 804, the machine 800 may include a single processor with asingle-core, a single processor with multiple cores (e.g., a multi-coreprocessor), multiple processors with a single core, multiple processorswith multiples cores, or any combination thereof.

The memory 806 includes a main memory 814, a static memory 816, and astorage unit 818, both accessible to the processors 804 via the bus 840.The main memory 806, the static memory 816, and storage unit 818 storethe instructions 810 embodying any one or more of the methodologies orfunctions described herein. The instructions 810 may also reside,completely or partially, within the main memory 814, within the staticmemory 816, within machine-readable medium 820 within the storage unit818, within at least one of the processors 804 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 800.

The I/O components 802 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 802 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 802 mayinclude many other components that are not shown in FIG. 8 . In variousexamples, the I/O components 802 may include user output components 826and user input components 828. The user output components 826 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 828 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 802 may include biometriccomponents 830, motion components 832, environmental components 834, orposition components 836, among a wide array of other components. Forexample, the biometric components 830 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 832 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 834 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the processors 804 may have a camera systemcomprising, for example, front cameras on a front surface of theprocessors 804 and rear cameras on a rear surface of the processors 804.The front cameras may, for example, be used to capture still images andvideo of a user of the processors 804 (e.g., “selfies”). The rearcameras may, for example, be used to capture still images and videos ina more traditional camera mode. In addition to front and rear cameras,the processors 804 may also include a 360° camera for capturing 360°photographs and videos.

Further, the camera system of a processors 804 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the processors 804. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera and a depth sensor, for example.

The position components 836 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 802 further include communication components 838operable to couple the machine 800 to a network 822 or devices 824 viarespective coupling or connections. For example, the communicationcomponents 838 may include a network interface component or anothersuitable device to interface with the network 822. In further examples,the communication components 838 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 824 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 838 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 838 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components838, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 814, static memory 816, andmemory of the processors 804) and storage unit 818 may store one or moresets of instructions and data structures (e.g., software) embodying orused by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 810), when executedby processors 804, cause various operations to implement the disclosedexamples.

The instructions 810 may be transmitted or received over the network822, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components838) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 810 maybe transmitted or received using a transmission medium via a coupling(e.g., a peer-to-peer coupling) to the devices 824.

Software Architecture

FIG. 9 is a block diagram 900 illustrating a software architecture 904,which can be installed on any one or more of the devices describedherein. The software architecture 904 is supported by hardware such as amachine 902 that includes processors 920, memory 926, and I/O components938. In this example, the software architecture 904 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 904 includes layerssuch as an operating system 912, libraries 910, frameworks 908, andapplications 906. Operationally, the applications 906 invoke API calls950 through the software stack and receive messages 952 in response tothe API calls 950.

The operating system 912 manages hardware resources and provides commonservices. The operating system 912 includes, for example, a kernel 914,services 916, and drivers 922. The kernel 914 acts as an abstractionlayer between the hardware and the other software layers. For example,the kernel 914 provides memory management, processor management (e.g.,scheduling), component management, networking, and security settings,among other functionality. The services 916 can provide other commonservices for the other software layers. The drivers 922 are responsiblefor controlling or interfacing with the underlying hardware. Forinstance, the drivers 922 can include display drivers, camera drivers,BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers,serial communication drivers (e.g., USB drivers), WI-FI® drivers, audiodrivers, power management drivers, and so forth.

The libraries 910 provide a common low-level infrastructure used by theapplications 906. The libraries 910 can include system libraries 918(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 910 can include APIlibraries 924 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 910 can also include a widevariety of other libraries 928 to provide many other APIs to theapplications 906.

The frameworks 908 provide a common high-level infrastructure that isused by the applications 906. For example, the frameworks 908 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 908 canprovide a broad spectrum of other APIs that can be used by theapplications 906, some of which may be specific to a particularoperating system or platform.

In an example, the applications 906 may include a home application 936,a contacts application 930, a browser application 932, a book readerapplication 934, a location application 942, a media application 944, amessaging application 946, a game application 948, and a broadassortment of other applications such as a third-party application 940.The applications 906 are programs that execute functions defined in theprograms. Various programming languages can be employed to create one ormore of the applications 906, structured in a variety of manners, suchas object-oriented programming languages (e.g., Objective-C, Java, orC++) or procedural programming languages (e.g., C or assembly language).In a specific example, the third-party application 940 (e.g., anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform)may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. Inthis example, the third-party application 940 can invoke the API calls950 provided by the operating system 912 to facilitate functionalitydescribed herein.

GLOSSARY

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In variousexamples, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering examples in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In examples in which multiple hardware componentsare configured or instantiated at different times, communicationsbetween such hardware components may be achieved, for example, throughthe storage and retrieval of information in memory structures to whichthe multiple hardware components have access. For example, one hardwarecomponent may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther hardware component may then, at a later time, access the memorydevice to retrieve and process the stored output. Hardware componentsmay also initiate communications with input or output devices, and canoperate on a resource (e.g., a collection of information). The variousoperations of example methods described herein may be performed, atleast partially, by one or more processors that are temporarilyconfigured (e.g., by software) or permanently configured to perform therelevant operations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented components that operateto perform one or more operations or functions described herein. As usedherein, “processor-implemented component” refers to a hardware componentimplemented using one or more processors. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented components. Moreover,the one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors, not only residing within asingle machine, but deployed across a number of machines. In someexamples, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

What is claimed is:
 1. A method, comprising: receiving plural UniformResource Locators (URLs), each URL of the plural URLs corresponding to arespective webpage of a website; accessing, from a database, a set ofterms, the set of terms having been predetermined as prioritized;extracting distinct terms corresponding to a path level, a query key anda cvar key for the plural URLs; computing a similarity score of thedistinct terms with the set of terms; identifying, based on thecomputing, URLs of the plural URLs having at least one term appearingwithin the set of terms; applying weights to the identified URLs, toprioritize the identified URLs relative to other URLs of the pluralURLs; generating, based on applying the weights, a distance matrix withpairwise distances between the plural URLs; performing hierarchicalclustering based on the distance matrix, to generate a dendrogram inwhich the plural URLs are arranged in hierarchical clusters;determining, based on a stored representation of the dendrogram, apredicted page group for each of the plural URLs; and causing, based ondetermining the predicted page group for each of the plural URLs,display of metrics corresponding to the website.
 2. The method of claim1, further comprising: computing URL patterns and conditions for eachcluster within the hierarchical clusters.
 3. The method of claim 1,wherein the hierarchical clustering comprises agglomerative hierarchicalclustering.
 4. The method of claim 1, wherein computing the similarityscore corresponds to calculating a respective levenshtein ratio for thepath level, the query key and the cvar key for the plural URLs, andwherein the identifying comprises selecting URLs of the plural URLs withlevenshtein ratios exceeding a predefined value with respect to the pathlevel, the query key and the cvar key.
 5. The method of claim 1, whereindetermining the hierarchical clusters comprises: performing gridsearching with respect to a preset range of clusters; computing asilhouette score for each cluster within the preset range of clusters;and determining the hierarchical clusters based on the computedsilhouette scores.
 6. The method of claim 5, further comprising:accessing, from a database, a set of terms, the set of terms having beenpredetermined as prioritized, wherein determining the hierarchicalclusters is further based on identifying nodes within the dendrogramhaving conditions that include at least one term within the set ofterms.
 7. A system comprising: a processor; and a memory storinginstructions that, when executed by the processor, configure theprocessor to perform operations comprising: receiving plural UniformResource Locators (URLs), each URL of the plural URLs corresponding to arespective webpage of a website; accessing, from a database, a set ofterms, the set of terms having been predetermined as prioritized;extracting distinct terms corresponding to a path level, a query key anda cvar key for the plural URLs; computing a similarity score of thedistinct terms with the set of terms; identifying, based on thecomputing, URLs of the plural URLs having at least one term appearingwithin the set of terms; applying weights to the identified URLs, toprioritize the identified URLs relative to other URLs of the pluralURLs; generating, based on applying the weights, a distance matrix withpairwise distances between the plural URLs; performing hierarchicalclustering based on the distance matrix, to generate a dendrogram inwhich the plural URLs are arranged in hierarchical clusters; anddetermining, based on a stored representation of the dendrogram, apredicted page group for each of the plural URLs; and causing, based ondetermining the predicted page group for each of the plural URLs,display of metrics corresponding to the website.
 8. The system of claim7, the operations further comprising: computing URL patterns andconditions for each cluster within the hierarchical clusters.
 9. Thesystem of claim 7, wherein the hierarchical clustering comprisesagglomerative hierarchical clustering.
 10. The system of claim 9,wherein computing the similarity score corresponds to calculating arespective levenshtein ratio for the path level, the query key and thecvar key for the plural URLs, and wherein the identifying comprisesselecting URLs of the plural URLs with levenshtein ratios exceeding apredefined value with respect to the path level, the query key and thecvar key.
 11. The system of claim 7, wherein determining thehierarchical clusters comprises: performing grid searching with respectto a preset range of clusters; computing a silhouette score for eachcluster within the preset range of clusters; and determining thehierarchical clusters based on the computed silhouette scores.
 12. Thesystem of claim 11, the operations further comprising: accessing, from adatabase, a set of terms, the set of terms having been predetermined asprioritized, wherein determining the hierarchical clusters is furtherbased on identifying nodes within the dendrogram having conditions thatinclude at least one term within the set of terms.
 13. A non-transitorycomputer-readable storage medium, the computer-readable storage mediumincluding instructions that when executed by a computer, cause thecomputer to perform operations comprising: receiving plural UniformResource Locators (URLs), each URL of the plural URLs corresponding to arespective webpage of a website; accessing, from a database, a set ofterms, the set of terms having been predetermined as prioritized;extracting distinct terms corresponding to a path level, a query key anda cvar key for the plural URLs; computing a similarity score of thedistinct terms with the set of terms; identifying, based on thecomputing, URLs of the plural URLs having at least one term appearingwithin the set of terms; applying weights to the identified URLs, toprioritize the identified URLs relative to other URLs of the pluralURLs; generating, based on applying the weights, a distance matrix withpairwise distances between the plural URLs; performing hierarchicalclustering based on the distance matrix, to generate a dendrogram inwhich the plural URLs are arranged in hierarchical clusters; anddetermining, based on a stored representation of the dendrogram, apredicted page group for each of the plural URLs; and causing, based ondetermining the predicted page group for each of the plural URLs,display of metrics corresponding to the website.
 14. The non-transitorycomputer-readable storage medium of claim 13, the operations furthercomprising: computing URL patterns and conditions for each clusterwithin the hierarchical clusters.
 15. The non-transitorycomputer-readable storage medium of claim 13, wherein the hierarchicalclustering comprises agglomerative hierarchical clustering.
 16. Thenon-transitory computer-readable storage medium of claim 13, whereincomputing the similarity score corresponds to calculating a respectivelevenshtein ratio for the path level, the query key and the cvar key forthe plural URLs, and wherein the identifying comprises selecting URLs ofthe plural URLs with levenshtein ratios exceeding a predefined valuewith respect to the path level, the query key and the cvar key.
 17. Thenon-transitory computer-readable storage medium of claim 13, whereindetermining the hierarchical clusters comprises: performing gridsearching with respect to a preset range of clusters; computing asilhouette score for each cluster within the preset range of clusters;and determining the hierarchical clusters based on the computedsilhouette scores.